Improved PESA algorithm based on comentropy

Aiming at the issue that the computational effort the complexity and the running time of PESA algorithm are increasing rapidly with the growth of the solutions set number, a comentropy-based PESA algorithm (C-PESA) by merg-ing the entropy value metric into PESA algorithm was proposed. According to t...

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Main Authors: Kun WANG, Lin-lin WANG, Yan LIU, Yu-hua ZHANG, Meng WU
Format: Article
Language:zho
Published: Editorial Department of Journal on Communications 2013-11-01
Series:Tongxin xuebao
Subjects:
Online Access:http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2013.11.005/
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author Kun WANG
Lin-lin WANG
Yan LIU
Yu-hua ZHANG
Meng WU
author_facet Kun WANG
Lin-lin WANG
Yan LIU
Yu-hua ZHANG
Meng WU
author_sort Kun WANG
collection DOAJ
description Aiming at the issue that the computational effort the complexity and the running time of PESA algorithm are increasing rapidly with the growth of the solutions set number, a comentropy-based PESA algorithm (C-PESA) by merg-ing the entropy value metric into PESA algorithm was proposed. According to the distributed characteristic of the entropy value metric over the Pareto solution set, the proposed algorithm could determine whether the population has developed to the mature stage, which is reached when the number iterations is 1 300 in C-PESA. Thereby, the optimization process can be finished as soon as possible, and in a certain extent, the time complexity of PESA was simplified. Simula-tion results show that the computational effort of C-PESA increases linearly with the rising number of solutions. Mean-while, the computation time is improved almost four times, and the evolutionary computation efficiency is also enhanced.
format Article
id doaj-art-682b35620fdd43ea9324c72dfafcbcd3
institution OA Journals
issn 1000-436X
language zho
publishDate 2013-11-01
publisher Editorial Department of Journal on Communications
record_format Article
series Tongxin xuebao
spelling doaj-art-682b35620fdd43ea9324c72dfafcbcd32025-08-20T02:34:39ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2013-11-0134334159834442Improved PESA algorithm based on comentropyKun WANGLin-lin WANGYan LIUYu-hua ZHANGMeng WUAiming at the issue that the computational effort the complexity and the running time of PESA algorithm are increasing rapidly with the growth of the solutions set number, a comentropy-based PESA algorithm (C-PESA) by merg-ing the entropy value metric into PESA algorithm was proposed. According to the distributed characteristic of the entropy value metric over the Pareto solution set, the proposed algorithm could determine whether the population has developed to the mature stage, which is reached when the number iterations is 1 300 in C-PESA. Thereby, the optimization process can be finished as soon as possible, and in a certain extent, the time complexity of PESA was simplified. Simula-tion results show that the computational effort of C-PESA increases linearly with the rising number of solutions. Mean-while, the computation time is improved almost four times, and the evolutionary computation efficiency is also enhanced.http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2013.11.005/evolutionary computationPESA algorithmmulti-objective optimizationcomentropy
spellingShingle Kun WANG
Lin-lin WANG
Yan LIU
Yu-hua ZHANG
Meng WU
Improved PESA algorithm based on comentropy
Tongxin xuebao
evolutionary computation
PESA algorithm
multi-objective optimization
comentropy
title Improved PESA algorithm based on comentropy
title_full Improved PESA algorithm based on comentropy
title_fullStr Improved PESA algorithm based on comentropy
title_full_unstemmed Improved PESA algorithm based on comentropy
title_short Improved PESA algorithm based on comentropy
title_sort improved pesa algorithm based on comentropy
topic evolutionary computation
PESA algorithm
multi-objective optimization
comentropy
url http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2013.11.005/
work_keys_str_mv AT kunwang improvedpesaalgorithmbasedoncomentropy
AT linlinwang improvedpesaalgorithmbasedoncomentropy
AT yanliu improvedpesaalgorithmbasedoncomentropy
AT yuhuazhang improvedpesaalgorithmbasedoncomentropy
AT mengwu improvedpesaalgorithmbasedoncomentropy